Hydrogenation catalyst target detection algorithm based on improved CenterNet

Zhujun Wang; Haobin Li

doi:10.1117/12.3011598

7 December 2023 Hydrogenation catalyst target detection algorithm based on improved CenterNet

Zhujun Wang, Haobin Li

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129410T (2023) https://doi.org/10.1117/12.3011598
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

In order to realize intelligent recognition and statistics of hydrogenation catalyst electron microscope image information and speed up electron microscope experiments. This paper proposes an improved CenterNet hydrogenation catalyst target detection algorithm, which uses Resnet50 backbone network to extract features, and adopts two sets of spatially separable convolution optimization residual modules of different scales to improve feature extraction capability. Moreover, FPN (Feature Pyramid Networks) is added to integrate shallow and deep features. In order to further solve the problem of background interference, the coordinate attention module is introduced to adjust the weight from adaptation, which can highlight the target characteristics of the catalyst more effectively. At the same time, the backbone network is optimized by referring to DenseNet in the downsampling stage, and on this basis, an improved ASPP module is added to adjust the sampling rate and increase the receptive field, so as to further improve the network's ability to identify and locate the target. Experimental results show that the AP value of the improved CenterNet model improved by 19.37, 20.16, 24.80, and 12.92 percentage points over the original CenterNet, YOLOv4, SSD, and Faster R-CNN detection algorithms in the same experimental setting. After the improvement, the detection accuracy of the model is obviously improved, and the model has a high capability of hydrogenation catalyst target detection.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhujun Wang and Haobin Li "Hydrogenation catalyst target detection algorithm based on improved CenterNet", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129410T (7 December 2023); https://doi.org/10.1117/12.3011598

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KEYWORDS

Convolution

Target detection

Feature extraction

Sampling rates

Object detection

Electron microscopes

Target recognition

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